Physics-Informed Neural Networks for Elliptical-Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau

Yunpeng Chen1,2; Sjoerd A. L. de Ridder2; Sebastian Rost2; Zhen Guo1; Xiaoyang Wu1; Shilin Li1; Yongshun Chen1

2023-12-27

10.1029/2023JB027378

JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH   影响因子和分区

2169-9313

2169-9356

["We develop a novel approach for multi-frequency, elliptical-anisotropic eikonal tomography based on physics-informed neural networks (pinnEAET). This approach simultaneously estimates the medium properties controlling anisotropic Rayleigh waves and reconstructs the traveltimes. The physics constraints built into pinnEAET's neural network enable high-resolution results with limited inputs by inferring physically plausible models between data points. Even with a single source, pinnEAET can achieve stable convergence on key features where traditional methods lack resolution. We apply pinnEAET to ambient noise data from a dense seismic array (ChinArray-Himalaya II) in the northeastern Tibetan Plateau with only 20 quasi-randomly distributed stations as sources. Anisotropic phase velocity maps for Rayleigh waves in the period range from 10-40 s are obtained by training on observed traveltimes. Despite using only about 3% of the total stations as sources, our results show low uncertainties, good resolution and are consistent with results from conventional tomography.","Anisotropy refers to the directional dependence of seismic wave velocities, which can arise from a variety of factors such as crystal alignment, stress fields, or fluid-filled cracks. Elliptical-anisotropic eikonal tomography is a variant of eikonal tomography that can be used to estimate medium properties and reconstructed traveltimes from ambient noise data. In this study, we propose a new algorithm to implement multi-frequency, elliptical-anisotropic eikonal tomography based on physics-informed neural networks (pinnEAET), which combine data-driven models with theory-based models that include physics constraints on the system. We apply this architecture to data from a dense seismic array deployed on the northeastern Tibetan Plateau. Our results can achieve at least the same resolution as traditional methods while requiring less traveltime data. This strategy can provide new insights into the seismic imaging in case of limited or noisy data.","We present a physics-informed deep learning eikonal tomography method for anisotropic velocity modelingThe algorithm incorporates wave physics to simultaneously process multi-frequency data, ensuring reliable tomographic modelsWe successfully recover the anisotropic velocity structure of the northeastern Tibet using less data than in traditional models"]

elliptical-anisotropy eikonal tomography anisotropy physics informed neural network deep learning surface waves Tibet

SCI

英语

NI论文

第一 ; 通讯

National Science Foundation of China["U1901602","41890814"] ; null[KQTD20170810111725321]

Geochemistry & Geophysics

Geochemistry & Geophysics

WOS:001131019300001

AMER GEOPHYSICAL UNION

GEOSCIENCES

人工提交

1.Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.School of Earth and Environment, University of Leeds, Leeds, UK

Yunpeng Chen,Sjoerd A. L. de Ridder,Sebastian Rost,et al. Physics-Informed Neural Networks for Elliptical-Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau[J]. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,2023,128(12).

Yunpeng Chen.,Sjoerd A. L. de Ridder.,Sebastian Rost.,Zhen Guo.,Xiaoyang Wu.,...&Yongshun Chen.(2023).Physics-Informed Neural Networks for Elliptical-Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau.JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,128(12).

Yunpeng Chen,et al."Physics-Informed Neural Networks for Elliptical-Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau".JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 128.12(2023).